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  1. Computation and the Sociological Imagination

    Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.

  2. Review Essay: See It with Figures

    The short story is that Kieran Healy’s Data Visualization: A Practical Introduction is a gentle introduction to the effective display of social science data using the R package ggplot2. It is beautifully put together, achingly clear, and effective.
  3. Black Homebuying after the Crisis: Appreciation Patterns in Fifteen Large Metropolitan Areas

    Some have questioned the financial wisdom of homeownership and, especially, Black homeownership. This is understandable because the mortgage crisis dealt heavy blows to Black homeowners. One concern is that home values may not appreciate as much where Blacks purchase homes. We examine how Black homebuyers fared compared to White and Latino buyers in terms of home appreciation during the 2012 to 2017 recovery. We examine appreciation rates by race and ethnicity across 15 metros.

  4. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

    We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.
  5. Work–Family Conflict and Well-Being among German Couples: A Longitudinal and Dyadic Approach

    This study examines dual-earner couples to determine whether changes in work–family conflict predict changes in one’s own (i.e., actor effects) or partner’s (i.e., partner effects) health and well-being as well as gender differences in these relationships.
  6. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  7. The Meaning of 'Theory'

    ‘Theory’ is one of the most important words in the lexicon of contemporary sociology. Yet, their ubiquity notwithstanding, it is quite unclear what sociologists mean by the words ‘theory,’ ‘theoretical,’ and ‘theorize.’ I argue that confusions about the meaning of ‘theory’ have brought about undesirable consequences, including conceptual muddles and even downright miscommunication. In this paper I tackle two questions: (a) what does ‘theory’ mean in the sociological language?; and (b) what ought ‘theory’ to mean in the sociological language? I proceed in five stages.

  8. Review Essay: Back to the Future

    In one of my undergraduate courses, I show students a photo of Paul Lazarsfeld and Frank Stanton. Of course, neither social scientist is familiar to them, but I argue to my students that Lazarsfeld had a bigger impact on the daily practice of sociology than any member of the Marx/Weber/Durkheim triumvirate they study in classical theory.

  9. The Purposes of Refugee Education: Policy and Practice of Including Refugees in National Education Systems

    This article explores the understood purposes of refugee education at global, national, and school levels. To do so, we focus on a radical shift in global policy to integrate refugees into national education systems and the processes of vernacularization accompanying its widespread implementation. We use a comparative case study approach; our dataset comprises global policy documents and original interviews (n = 147) and observations in 14 refugee-hosting nation-states.
  10. A General Framework for Comparing Predictions and Marginal Effects across Models

    Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models.